Machine Learning Based Password Strength Analysis
Creators
- 1. Assistant Professor, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
- 2. Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
- 3. Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
Contributors
Contact person:
- 1. Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
Description
Abstract: Passwords, as the most used method of authentication because to its ease of implementation, allow attackers to get access to the accounts owned by others by means of cracking passwords. This is cause of the similar patterns that users use to create a password, like dictionary words, common phrases, person and location names, keyboard pattern, and so on. Multiple password cracking techniques had been introduced to predict the password offline or online, with the majority of records say the one with weak password or familiar password patterns being cracked. This suggested prototype implements numerous machine learning methods such as Decision Tree (DT), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) on a web application in real time to force users to choose a secure password. This results in the user's account being logged into if particularly the password strength from more than half of the algorithms is strong.
Notes
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Additional details
Related works
- Is cited by
- Journal article: 2278-3075 (ISSN)
References
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Subjects
- ISSN: 2278-3075 (Online)
- https://portal.issn.org/resource/ISSN/2278-3075#
- Retrieval Number: 100.1/ijitee.H91190711822
- https://www.ijitee.org/portfolio-item/h91190711822/
- Journal Website: www.ijitee.org
- https://www.ijitee.org/
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org/